def regular_spectrum_graph(line):
allspec = ('PHOENIX/%(allspec)s' %{'allspec' : line})
contspec = ('PHOENIXcont/%(cont)s' %{'cont' : cont_file[0]})
a = line.split('-')
#Calls in the file from the directory
spectrum = load_spec(allspec)
conspec = load_spec(contspec)
newgrid = np.arange(3000,12000,0.01)
conspecflux = np.interp(newgrid, conspec[:,0], conspec[:,1])
specflux = np.interp(newgrid, spectrum[:,0], spectrum[:,1])
conspecflux = convolve(conspecflux, 0.15)
specflux = convolve(specflux, 0.15)
divflux = specflux/conspecflux
plt.title('Spectrum Comparison For nlte and lte Effects \n On a T_eff = 4500K Star \n Smoothed by convolution with gaussian curve FWHM = 0.15 Angstroms'.format(a[1]))
plt.xlabel('Wavelength (Angstroms)')
plt.ylabel('Normalized Flux $(Flux/1.2*10^{14})$')
plt.grid(True)
#plt.plot(spectrum[:,0], spectrum[:,1])
plt.plot(newgrid, divflux) # ,label = '%(type)s, $[Fe/H]$ = -%(metal)s' %{ 'type' :a[0], 'metal' :a[3][0:3]})
def Relative_spec(nltespec, ltespec):
'''
FUNCTION CURRENTLY DOES NOT WORK PROPERLY!!!
THIS IS AN ARTIFACT OF ME CREATING THE .norm.7 FILES
TO INCREASE PROGRAM SPEED.
'''
model = array(Spec_Normalize(nltespec, ltespec)).T
a = nltespec.split('-')
#b = ltespec.split('-')
#Modelspecfile = ('PHOENIX/%(allspec)s' %{'allspec' : model})
Arcturusspec = ltespec
specgrid = np.arange(3000, 12000, 0.01)
ModelSpec = model
Arcturus = Arcturusspec
ModelSpecFlux = np.interp(specgrid, ModelSpec[:, 0], ModelSpec[:, 1])
ArcturusFlux = np.interp(specgrid, Arcturus[:, 0], Arcturus[:, 1])
print 'Plotting {} model, Teff= {}K '.format(
a[0], a[1]), '[Fe/H]= -{}'.format(a[3][0:3]), 'iter=', i
ModelSpec2 = convolve(ModelSpecFlux, macroturb, 0.01) #Macroturbulence
ModelSpec3 = convolve(ModelSpec2, 2, 0.01) #Spectrograph
RelativeSpec = 100. * (ModelSpec3 - ArcturusFlux) / ArcturusFlux
Unity = array((specgrid, RelativeSpec)).T
Unity2 = Unity[(Unity[:, 0] <= 7300)]
SpecSigma = np.std(Unity2[:, 1])
SpecMean = np.mean(Unity[:, 1])
print 'Spectrum Mean= ', SpecMean
print 'Standard Deviation= ', SpecSigma
plt.plot(Unity2[:, 0],
Unity2[:, 1],
label='[Teff] = {}K, model={}, cTeff= {}K'.format(
a[1], a[0], b[1]))
#plt.ylim(-100,1000)
plt.xlim(linecentre - dx, linecentre + dx)
plt.grid(True)
plt.legend()
plt.ylabel('$(F_{NLTE} - F_{OBS})/F_{OBS}$ (%)')
plt.xlabel('Wavelength, ($\AA$)')
plt.title(
'Relative Spectrum Differences \n Between Model and Observed Spectra for Arcturus \n '
)
return RelativeSpec
def Relative_spec(xmin, xmax, i):
scales = [0.995,1.02,0.997,1.02,0.997,1.02]
ModelSpec = array(Spec_Normalize(file_list[i],cont_file[i//2])).T
Arcturus = arcturus_link()
a = file_list[i].split('-')
#Modelspecfile = ('PHOENIX/%(allspec)s' %{'allspec' : model})
specgrid = np.arange(xmin, xmax, 0.01)
Arcturus = Arcturus[(Arcturus[:,0] >= xmin) & (Arcturus[:,0] <= xmax)]
ModelSpec = ModelSpec[(ModelSpec[:,0] >= xmin) &
(ModelSpec[:,0] <= xmax) ]
ModelSpecFlux = np.interp(specgrid, ModelSpec[:,0], ModelSpec[:,1])
ArcturusFlux = np.interp(specgrid, Arcturus[:,0] , Arcturus[:,1])
ModelSpec2 = convolve(ModelSpecFlux, 6 , 0.01)
RelativeSpec = 100.*(ModelSpec2/scales[i] - ArcturusFlux)/ArcturusFlux
Unity = array((specgrid, RelativeSpec )).T
#Unity = Unity[(Unity[:,1] <= 1000 ) & (Unity[:,1] >= -1000)]
plt.plot(Unity[:,0], Unity[:,1], label = 'Type: {}, T = {}K'.format(a[0],a[1]))
plt.ylim(-100,1000)
plt.grid(True)
plt.legend()
plt.ylabel('Relative Flux')
plt.xlabel('Wavelength, ($\AA$)')
def Relative_spec(nltespec, ltespec):
model = array(Spec_Normalize(file_list[i], cont_file[i // 2])).T
observed = ltespec
a = file_list[i].split("-")
b = cont_file[i // 2].split("-")
# Modelspecfile = ('PHOENIX/%(allspec)s' %{'allspec' : model})
Arcturusspec = observed
specgrid = np.arange(3000, 12000, 0.01)
ModelSpec = model
Arcturus = Arcturusspec
ModelSpecFlux = np.interp(specgrid, ModelSpec[:, 0], ModelSpec[:, 1])
ArcturusFlux = np.interp(specgrid, Arcturus[:, 0], Arcturus[:, 1])
print "Plotting {} model, Teff= {}K ".format(a[0], a[1]), "[Fe/H]= -{}".format(a[3][0:3]), "iter=", i
ModelSpec2 = convolve(ModelSpecFlux, macroturb, 0.01) # Macroturbulence
ModelSpec3 = convolve(ModelSpec2, 2, 0.01) # Spectrograph
RelativeSpec = 100.0 * (ModelSpec3 - ArcturusFlux) / ArcturusFlux
Unity = array((specgrid, RelativeSpec)).T
Unity2 = Unity[(Unity[:, 0] <= 7300)]
SpecSigma = np.std(Unity2[:, 1])
SpecMean = np.mean(Unity[:, 1])
print "Spectrum Mean= ", SpecMean
print "Standard Deviation= ", SpecSigma
plt.plot(Unity2[:, 0], Unity2[:, 1], label="[Teff] = {}K, model={}, cTeff= {}K".format(a[1], a[0], b[1]))
# plt.ylim(-100,1000)
plt.xlim(linecentre - dx, linecentre + dx)
plt.grid(True)
plt.legend()
plt.ylabel("$(F_{NLTE} - F_{OBS})/F_{OBS}$ (%)")
plt.xlabel("Wavelength, ($\AA$)")
plt.title("Relative Spectrum Differences \n Between Model and Observed Spectra for Arcturus \n ")
return RelativeSpec
def regular_spectrum_graph(specfile,continuum):
allspec = ('%(allspec)s' %{'allspec' : specfile})
contspec = ('%(cont)s' %{'cont' : continuum})
scalefactor = [0.995,1.02,0.997,1.02,0.997,1.02]
a = specfile.split('-')
#Calls in the file from the directory
spectrum = load_spec(allspec,'PHOENIX')
conspec = load_spec(contspec, 'PHOENIXcont')
newgrid = np.arange(3000,12000,0.01)
conspecflux = np.interp(newgrid, conspec[:,0], conspec[:,1])
specflux = np.interp(newgrid, spectrum[:,0], spectrum[:,1])
macroturb = 6
spectrograph = 2
conspecflux2 = convolve(conspecflux, macroturb,0.01) #Macroturbulence
conspecflux3 = convolve(conspecflux2, spectrograph,0.01) #Spectrograph resolution
specflux2 = convolve(specflux, FWHM,0.01)
specflux3 = convolve(specflux2, FWHM2, 0.01)
divflux = specflux3/conspecflux3
plt.title('Observed Arcturus Spectrum \n With 4250K Alpha enhanced model comparison \n Smoothed by convolution with gaussian curve FWHM = 5/150 Angstroms')
plt.xlabel('Wavelength ($\AA$)')
plt.ylabel('Normalized Flux (unitless)')
plt.grid(True)
plt.plot(newgrid, divflux/scalefactor[i], label = '%(type)s, $T_{eff}$ = %(metal)sK' %{ 'type' :a[0], 'metal' :a[1]})
plt.xlim(7149,7150.8)
plt.legend()
def Spec_Graph():
allspec2 = ('PHOENIX/%(allspec)s' %{'allspec' : file_list[2*i]}) #nlte Spectrum
allspec1 = ('PHOENIX/%(allspec)s' %{'allspec' : file_list[2*i +1]}) #lte Spectrum
g = file_list[2*i].split('-')
Spec1 = np.genfromtxt(allspec1, dtype ='str', usecols = (0,1))
DtoE(Spec1[:,0])
DtoE(Spec1[:,1])
Spec1 = np.array(Spec1, dtype = float)
Spec2 = np.genfromtxt(allspec2, dtype = 'str', usecols = (0,1))
DtoE(Spec2[:,0])
DtoE(Spec2[:,1])
Spec2 = np.array(Spec2, dtype = float)
Spec1[:,1] = Spec1[:,1][np.argsort(Spec1[:,0])]
Spec1[:,0] = np.sort(Spec1[:,0])
Spec2[:,1] = Spec2[:,1][np.argsort(Spec2[:,0])]
Spec2[:,0] = np.sort(Spec2[:,0])
Spec1[:,1] = 10**Spec1[:,1]
Spec2[:,1] = 10**Spec2[:,1]
newgrid = np.arange(3000,12000, 0.01) #Uniform grid space for each spectra
NewSpec1 = np.interp(newgrid, Spec1[:,0], Spec1[:,1])
NewSpec2 = np.interp(newgrid, Spec2[:,0], Spec2[:,1])
##### Fast Fourier Transform convolution function #####
FWHM = 0.15
smooth1 = convolve(NewSpec1,FWHM)
smooth2 = convolve(NewSpec2,FWHM)
RelativeSpectrum = ( (smooth1 - smooth2)/smooth2 ) * 100. #Relative fluxes as a percent
SpecAvg = np.mean(RelativeSpectrum)
SpecSigma = np.std(RelativeSpectrum)
#top lines
plt.hlines(SpecAvg + SpecSigma, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 1.0)
plt.hlines(SpecAvg - SpecSigma, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 1.0)
plt.hlines(SpecAvg, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 2.0)
SmoothedSpec = np.array([newgrid, RelativeSpectrum], dtype= float)
SmoothedSpec = SmoothedSpec.T
SpecFeatures = list()
for q in range(0,len(SmoothedSpec)):
if (SmoothedSpec[q,1] >= SpecAvg + SpecSigma) or (SmoothedSpec[q,1] <= SpecAvg - SpecSigma):
SpecFeatures.append([SmoothedSpec[q,0],SmoothedSpec[q,1]])
SpecFeatures = np.array(SpecFeatures, dtype=float)
##### This finds where the important lines are located on the convolved relative spectrum.
nothing = [] # Temporary list that is not useful after plotting
for m in range(0,len(Linelist)):
a = Linelist['wavelength'][m] - 0.005
b = Linelist['wavelength'][m] + 0.005
linefile = SmoothedSpec[:,0]
nothing = np.append(nothing, np.where((linefile >= a) & (linefile <= b)))
##### Array made from y-value at line wavelength on each spectrum.
arrowlines = []
for n in range(0,len(nothing)):
#arrowlines.append([Linelist['wavelength'][n], SmoothedSpec[nothing[n],1], Linelist['element'][n], Linelist['ion'][n]])
arrowlines.append([Linelist['wavelength'][n], SmoothedSpec[nothing[n],1]])
#arrowlines = np.array(arrowlines, dtype= ([('wavelength','f8'), ('percent','f8'), ('element','a2'), ('ion','a2') ]))
arrowlines = np.array(arrowlines, dtype= float)
arrowlines[:,1] = arrowlines[:,1][np.argsort(arrowlines[:,0])]
arrowlines[:,0] = np.sort(arrowlines[:,0])
#plt.plot(arrowlines[:,0], arrowlines[:,1], c= colourstring[i], linewidth= 2.0,
# label = ' Fe/H = -%(fehs)s ' %{'fehs' : g[3][0:3]} )
plt.scatter(arrowlines[:,0], arrowlines[:,1], c=colourstring[i])
if (g[3][0:3] == '0.5'):
for r in range(len(Linelist)):
#if (arrowlines[r,1] >= 10.):
plt.annotate('%(element)s - %(ion)s, %(wave)s'
%{ 'element' : Linelist['element'][r],
'ion' : Linelist['ion'][r],
'wave' : Linelist['wavelength'][r] } ,
xy=(Linelist['wavelength'][r], SmoothedSpec[nothing[r],1]),
xytext=(Linelist['wavelength'][r], SmoothedSpec[nothing[r],1] + 3 ),
arrowprops=dict(facecolor='black',arrowstyle ='->'))
'''
for s in range(len(stronglines)):
plt.annotate(atomlist[s] + ', {}'.format(stronglines[s,0]) ,
xy=(stronglines[s,0], 0),
xytext=(stronglines[s,0],80),
arrowprops=dict(facecolor='black', arrowstyle= '->'))
'''
plt.title('Locations of the Important Spectral Lines and the Changes due to NLTE Effects'
'\n $T_{eff}$ = %(teff)sK '
'\n %(fwhm)s$\AA$ Convolution'
%{ 'teff' : g[1] , 'fwhm': FWHM})
plt.grid(True)
plt.ylabel('$(F_{nlte} - F_{lte})/F_{lte}$ * 100%')
plt.xlabel('Wavelength ($\AA$)')
#Overplotting the locations of important spectral lines in the spectral range.
#for j in range(len(Linelist)):
# plt.annotate('%(element)s - %(ion)s' %{ 'element' : Linelist['element'][j], 'ion' : Linelist['ion'][j] } ,
# xy=(Linelist['wavelength'][j], -10),
# xytext=(Linelist['wavelength'][j], 80),
# arrowprops=dict(facecolor='red', headwidth = 0, linewidth = 0)
# )
plt.fill_between(newgrid,y1=SpecAvg + SpecSigma, y2 = SpecAvg-SpecSigma,color = 'y')
plt.plot(newgrid, RelativeSpectrum, c= colourstring[i], label = ' Fe/H = -%(fehs)s ' %{'fehs' : g[3][0:3]}, linewidth = 2.0)
plt.scatter(SpecFeatures[:,0], SpecFeatures[:,1], c = 'r')
#plt.plot(SpecFeatures[:,0], SpecFeatures[:,1], c = 'b', label = 'Significant lines')
plt.legend()
plt.xlim(3000, 9000)
return array(SpecFeatures), array(arrowlines)
def Relative_spec(nltespec, ltespec):
'''
FUNCTION CURRENTLY DOES NOT WORK PROPERLY!!!
THIS IS AN ARTIFACT OF ME CREATING THE .norm.7 FILES
TO INCREASE PROGRAM SPEED.
'''
model = array(Spec_Normalize(nltespec,ltespec)).T
a = nltespec.split('-')
#b = ltespec.split('-')
#Modelspecfile = ('PHOENIX/%(allspec)s' %{'allspec' : model})
Arcturusspec = ltespec
specgrid =np.arange(3000,12000,0.01)
ModelSpec = model
Arcturus = Arcturusspec
ModelSpecFlux = np.interp(specgrid, ModelSpec[:,0], ModelSpec[:,1])
ArcturusFlux = np.interp(specgrid, Arcturus[:,0] , Arcturus[:,1])
print 'Plotting {} model, Teff= {}K '.format(a[0],a[1]), '[Fe/H]= -{}'.format(a[3][0:3]) ,'iter=', i
ModelSpec2 = convolve(ModelSpecFlux, macroturb , 0.01) #Macroturbulence
ModelSpec3 = convolve(ModelSpec2, 2 , 0.01) #Spectrograph
RelativeSpec = 100.*(ModelSpec3 - ArcturusFlux)/ArcturusFlux
Unity = array((specgrid, RelativeSpec )).T
Unity2 = Unity[(Unity[:,0] <= 7300 )]
SpecSigma = np.std(Unity2[:,1])
SpecMean = np.mean(Unity[:,1])
print 'Spectrum Mean= ',SpecMean
print 'Standard Deviation= ',SpecSigma
plt.plot(Unity2[:,0], Unity2[:,1], label = '[Teff] = {}K, model={}, cTeff= {}K'.format(a[1],a[0],b[1]))
#plt.ylim(-100,1000)
plt.xlim(linecentre - dx, linecentre + dx)
plt.grid(True)
plt.legend()
plt.ylabel('$(F_{NLTE} - F_{OBS})/F_{OBS}$ (%)')
plt.xlabel('Wavelength, ($\AA$)')
plt.title('Relative Spectrum Differences \n Between Model and Observed Spectra for Arcturus \n ')
return RelativeSpec
def Spec_Graph(spectra):
allspec1 = ('%(allspec)s' % {'allspec': spectra[1]}) #nlte Spectrum
allspec2 = ('%(allspec)s' % {'allspec': spectra[0]}) #lte Spectrum
g = allspec1.split('-')
gg = allspec2.split('-')
print 'plotting F_{} - F_{} / F{} relative difference.'.format(
g[0], gg[0], gg[0])
Spec1 = load_spec(allspec1, 'PHOENIX')
Spec2 = load_spec(allspec2, 'PHOENIX')
newgrid = np.arange(3000, 12000,
0.01) #Uniform grid space for each spectra
NewSpec1 = np.interp(newgrid, Spec1[:, 0], Spec1[:, 1])
NewSpec2 = np.interp(newgrid, Spec2[:, 0], Spec2[:, 1])
##### Fast Fourier Transform convolution function #####
FWHM = 0.015
smooth1 = convolve(NewSpec1, FWHM, 0.01)
smooth2 = convolve(NewSpec2, FWHM, 0.01)
RelativeSpectrum = (
(smooth1 - smooth2) / smooth2) * 100. #Relative fluxes as a percent
SpecAvg = np.mean(RelativeSpectrum)
SpecSigma = np.std(RelativeSpectrum)
#top lines
#plt.hlines(SpecAvg + SpecSigma, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 1.0)
#plt.hlines(SpecAvg - SpecSigma, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 1.0)
#plt.hlines(SpecAvg, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 2.0)
SmoothedSpec = np.array([newgrid, RelativeSpectrum], dtype=float)
SmoothedSpec = SmoothedSpec.T
SpecFeatures = list()
for q in range(0, len(SmoothedSpec)):
if (SmoothedSpec[q, 1] >= SpecAvg + SpecSigma) or (
SmoothedSpec[q, 1] <= SpecAvg - SpecSigma):
SpecFeatures.append([SmoothedSpec[q, 0], SmoothedSpec[q, 1]])
SpecFeatures = np.array(SpecFeatures, dtype=float)
##### This finds where the important lines are located on the convolved relative spectrum.
nothing = [] # Temporary list that is not useful after plotting
for m in range(0, len(Linelist)):
a = Linelist['wavelength'][m] - 0.005
b = Linelist['wavelength'][m] + 0.005
linefile = SmoothedSpec[:, 0]
nothing = np.append(nothing,
np.where((linefile >= a) & (linefile <= b)))
##### Array made from y-value at line wavelength on each spectrum.
arrowlines = []
for n in range(0, len(nothing)):
#arrowlines.append([Linelist['wavelength'][n], SmoothedSpec[nothing[n],1], Linelist['element'][n], Linelist['ion'][n]])
arrowlines.append(
[Linelist['wavelength'][n], SmoothedSpec[nothing[n], 1]])
#arrowlines = np.array(arrowlines, dtype= ([('wavelength','f8'), ('percent','f8'), ('element','a2'), ('ion','a2') ]))
arrowlines = np.array(arrowlines, dtype=float)
arrowlines[:, 1] = arrowlines[:, 1][np.argsort(arrowlines[:, 0])]
arrowlines[:, 0] = np.sort(arrowlines[:, 0])
#plt.plot(arrowlines[:,0], arrowlines[:,1], c= colourstring[i], linewidth= 2.0,
# label = ' Fe/H = -%(fehs)s ' %{'fehs' : g[3][0:3]} )
#plt.scatter(arrowlines[:,0], arrowlines[:,1], c=colourstring[i])
'''
if (g[3][0:3] == '0.5'):
for r in range(len(Linelist)):
#if (arrowlines[r,1] >= 10.):
plt.annotate('%(element)s - %(ion)s, %(wave)s'
%{ 'element' : Linelist['element'][r],
'ion' : Linelist['ion'][r],
'wave' : Linelist['wavelength'][r] } ,
xy=(Linelist['wavelength'][r], SmoothedSpec[nothing[r],1]),
xytext=(Linelist['wavelength'][r], SmoothedSpec[nothing[r],1] + 60 ),
arrowprops=dict(facecolor='black',arrowstyle ='->'))
'''
'''
for s in range(len(stronglines)):
plt.annotate(atomlist[s] + ', {}'.format(stronglines[s,0]) ,
xy=(stronglines[s,0], 0),
xytext=(stronglines[s,0],80),
arrowprops=dict(facecolor='black', arrowstyle= '->'))
'''
plt.title('Teff = {}K ' ' {}$\AA$ Convolution'.format(g[1], FWHM))
plt.grid(True)
plt.ylabel('Relative Flux (%)')
plt.xlabel('Wavelength ($\AA$)')
#Overplotting the locations of important spectral lines in the spectral range.
'''
for j in range(len(Linelist)):
plt.annotate('%(element)s - %(ion)s' %{ 'element' : Linelist['element'][j], 'ion' : Linelist['ion'][j] } ,
xy=(Linelist['wavelength'][j], -10),
xytext=(Linelist['wavelength'][j], 80),
arrowprops=dict(facecolor='black', headwidth = 0, linewidth = 0)
)
'''
#plt.fill_between(newgrid,y1=SpecAvg + SpecSigma, y2 = SpecAvg-SpecSigma,color = 'y')
plt.plot(newgrid,
RelativeSpectrum,
c=colourstring[i],
label=' Fe/H = -%(fehs)s ' % {'fehs': g[3][0:3]},
linewidth=2.0)
#plt.scatter(SpecFeatures[:,0], SpecFeatures[:,1], c = 'r')
#plt.plot(SpecFeatures[:,0], SpecFeatures[:,1], label = 'Significant lines')
plt.legend()
plt.xlim(3000, 9000)
return array(SpecFeatures), array(arrowlines)
def Spec_Graph(spectra):
allspec1 = ('%(allspec)s' %{'allspec' : spectra[1]}) #nlte Spectrum
allspec2 = ('%(allspec)s' %{'allspec' : spectra[0]}) #lte Spectrum
g = allspec1.split('-')
gg = allspec2.split('-')
print 'plotting F_{} - F_{} / F{} relative difference.'.format(g[0],gg[0],gg[0])
Spec1 = load_spec(allspec1,'PHOENIX')
Spec2 = load_spec(allspec2,'PHOENIX')
newgrid = np.arange(3000,12000, 0.01) #Uniform grid space for each spectra
NewSpec1 = np.interp(newgrid, Spec1[:,0], Spec1[:,1])
NewSpec2 = np.interp(newgrid, Spec2[:,0], Spec2[:,1])
##### Fast Fourier Transform convolution function #####
FWHM = 0.015
smooth1 = convolve(NewSpec1,FWHM,0.01)
smooth2 = convolve(NewSpec2,FWHM,0.01)
RelativeSpectrum = ( (smooth1 - smooth2)/smooth2 ) * 100. #Relative fluxes as a percent
SpecAvg = np.mean(RelativeSpectrum)
SpecSigma = np.std(RelativeSpectrum)
#top lines
#plt.hlines(SpecAvg + SpecSigma, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 1.0)
#plt.hlines(SpecAvg - SpecSigma, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 1.0)
#plt.hlines(SpecAvg, 3000,9000, linestyle = '--', colors = colourstring[i], lw = 2.0)
SmoothedSpec = np.array([newgrid, RelativeSpectrum], dtype= float)
SmoothedSpec = SmoothedSpec.T
SpecFeatures = list()
for q in range(0,len(SmoothedSpec)):
if (SmoothedSpec[q,1] >= SpecAvg + SpecSigma) or (SmoothedSpec[q,1] <= SpecAvg - SpecSigma):
SpecFeatures.append([SmoothedSpec[q,0],SmoothedSpec[q,1]])
SpecFeatures = np.array(SpecFeatures, dtype=float)
##### This finds where the important lines are located on the convolved relative spectrum.
nothing = [] # Temporary list that is not useful after plotting
for m in range(0,len(Linelist)):
a = Linelist['wavelength'][m] - 0.005
b = Linelist['wavelength'][m] + 0.005
linefile = SmoothedSpec[:,0]
nothing = np.append(nothing, np.where((linefile >= a) & (linefile <= b)))
##### Array made from y-value at line wavelength on each spectrum.
arrowlines = []
for n in range(0,len(nothing)):
#arrowlines.append([Linelist['wavelength'][n], SmoothedSpec[nothing[n],1], Linelist['element'][n], Linelist['ion'][n]])
arrowlines.append([Linelist['wavelength'][n], SmoothedSpec[nothing[n],1]])
#arrowlines = np.array(arrowlines, dtype= ([('wavelength','f8'), ('percent','f8'), ('element','a2'), ('ion','a2') ]))
arrowlines = np.array(arrowlines, dtype= float)
arrowlines[:,1] = arrowlines[:,1][np.argsort(arrowlines[:,0])]
arrowlines[:,0] = np.sort(arrowlines[:,0])
#plt.plot(arrowlines[:,0], arrowlines[:,1], c= colourstring[i], linewidth= 2.0,
# label = ' Fe/H = -%(fehs)s ' %{'fehs' : g[3][0:3]} )
#plt.scatter(arrowlines[:,0], arrowlines[:,1], c=colourstring[i])
'''
if (g[3][0:3] == '0.5'):
for r in range(len(Linelist)):
#if (arrowlines[r,1] >= 10.):
plt.annotate('%(element)s - %(ion)s, %(wave)s'
%{ 'element' : Linelist['element'][r],
'ion' : Linelist['ion'][r],
'wave' : Linelist['wavelength'][r] } ,
xy=(Linelist['wavelength'][r], SmoothedSpec[nothing[r],1]),
xytext=(Linelist['wavelength'][r], SmoothedSpec[nothing[r],1] + 60 ),
arrowprops=dict(facecolor='black',arrowstyle ='->'))
'''
'''
for s in range(len(stronglines)):
plt.annotate(atomlist[s] + ', {}'.format(stronglines[s,0]) ,
xy=(stronglines[s,0], 0),
xytext=(stronglines[s,0],80),
arrowprops=dict(facecolor='black', arrowstyle= '->'))
'''
plt.title('Teff = {}K '' {}$\AA$ Convolution'.format(g[1],FWHM))
plt.grid(True)
plt.ylabel('Relative Flux (%)')
plt.xlabel('Wavelength ($\AA$)')
#Overplotting the locations of important spectral lines in the spectral range.
'''
for j in range(len(Linelist)):
plt.annotate('%(element)s - %(ion)s' %{ 'element' : Linelist['element'][j], 'ion' : Linelist['ion'][j] } ,
xy=(Linelist['wavelength'][j], -10),
xytext=(Linelist['wavelength'][j], 80),
arrowprops=dict(facecolor='black', headwidth = 0, linewidth = 0)
)
'''
#plt.fill_between(newgrid,y1=SpecAvg + SpecSigma, y2 = SpecAvg-SpecSigma,color = 'y')
plt.plot(newgrid, RelativeSpectrum, c= colourstring[i], label = ' Fe/H = -%(fehs)s ' %{'fehs' : g[3][0:3]}, linewidth = 2.0)
#plt.scatter(SpecFeatures[:,0], SpecFeatures[:,1], c = 'r')
#plt.plot(SpecFeatures[:,0], SpecFeatures[:,1], label = 'Significant lines')
plt.legend()
plt.xlim(3000, 9000)
return array(SpecFeatures), array(arrowlines)
